Matthias Bethge

My research goal is to understand how the brain makes sense of its high-dimensional sensory input. In particular, I seek to understand the formation of distributed neural representations in the visual system by studying deep neural networks, natural image statistics, unsupervised learning, and neural population coding, and by developing new data-analysis tools.

Judith Lam

At the Bernstein Center Tübingen, scientists from various disciplines, including theoretical and experimental neurobiology, machine learning, and medicine, collaborate in order to analyze the basis of inference processes in the brain. In particular, a main research goal is to understand the coordinated interaction of neurons during information processing.

Isabel Suditsch

Alexander Ecker

I want understand how neural systems perform visual perception. At the interface of computer vision and neuroscience, I try to understand both how the human visual system works and how to teach computers to make sense of images. I use an interdisciplinary approach that combines methods from machine learning and computer vision with behavioral studies and neuronal population recordings in the brain. My work is driven by the idea that we can advance artificial intelligence by understanding how biological systems implement intelligent behavior.

Wieland Brendel

My research goal is to close the gap between the visual information processing in humans and machines. One of the most striking differences is the susceptibility of Deep Neural Networks (DNNs) to almost imperceptible perturbations of their inputs. Getting machines closer to humans will require fundamentally new concepts to learn causal models of the world. My work aims to quantify the robustness of DNNs, to identify the causes for their susceptibility and to devise solutions by drawing inspiration from Neuroscience and Computer Vision.

Alexander Mathis

I am a Marie Curie fellow in the lab of Professor Venkatesh Murthy at
the Department of Molecular and Cellular Biology at Harvard University
and with Professor Matthias Bethge at the Bernstein Center for
Computational Neuroscience at the University of Tuebingen. My main
research interests comprise active sensing, odor-guided navigation and
optimal coding.

Tom Wallis

How do we visually experience the world around us in a coherent way, simply from the patterns of photons hitting our retinae? While it seems effortless to us, the formation of this representation by the brain is decidedly difficult to explain. I am working with Prof. Bethge and Prof. Felix Wichmann investigating the mechanisms of visual representation using psychophysics and computational modelling.

Santiago Cadena

Christina Funke

Robert Geirhos

Are human and machine vision relying on similar strategies for visual processing?
I use psychophysical methods to better understand machine vision, and convolutional neural networks to model aspects of human visual processing. A current focus of my work is robustness: What can machine vision learn from the incredibly general robustness of the human visual system towards distortions of any kind?

David Klindt

My research focuses on robust biological vision. Neuroscience has provided incredible inductive biases that led to breakthroughs in deep learning and computer vision. For instance, by suggesting hierarchical retinotopic visual processing in the brain that inspired the development of modern CNNs. Now computer vision has caught up and even excelled human performance in many different visual tasks. I am trying to deploy the lessons learned in deep learning and bring them back to visual neuroscience, e.g. with the idea of feature maps in the retina that share computations across types of neurons. Simultaneously, the computations we are still discovering in biological vision need to be explored further, e.g. to see how they can make the current generation of CNNs more robust to adversarial perturbations and more human-like in their visual understanding of the world.

Matthias Kümmerer

Learning what properties of an image are associated with human gaze placement is important both for understanding how biological systems explore the environment and for computer vision applications. Recent advances in deep learning for the first time enable us to explain a significant portion of the information expressed in the spatial fixation structure. My interest is twofold: I want to create better models for predicting human fixations in different tasks and on the other hand make use of these models to increase our understanding of how humans perform this task from a neuroscientific and psychophysical standpoint.

Claudio Michaelis

Humans do not only excel at acquiring novel concepts from a single demonstration but can also readily identify or reproduce them. When shown a new object humans have no problem pointing at similar objects or drawing their outlines. My goal is to bring similar capabilities to computer vision systems.

Jonas Rauber

I want to build more intelligent machines that improve our lives, and doing so by taking inspiration from humans: learning with little supervision, being robust to perturbations, generalizing across tasks, combining modalities, or utilizing hierarchical relationships. In particular, my current research focuses on understanding and improving the robustness of deep neural networks to adversarial perturbations.

Evgenia Rusak

To enable a future where autonomous cars can replace human drivers, we have to ensure that the autonomous agents make the right decisions at all times. In particular, bad weather scenarios currently pose a big problem for important tasks such as object detection and scene understanding. In my PhD, I work on improving the robustness of Deep Neural Nets to natural distortions such as rain or snow.

Lukas Schott

I aim to explore and narrow the gap between human and machine perception. My current focus is on adversarial examples: minimal and humanly almost imperceptible image perturbations which derail neural network predictions. This can also be viewed as a worst case of generalization. One goal is to overcome this problem by more fundamentally adapting the information processing in neural networks using feedback connections to perform an analysis by synthesis. This work involves probabilistic generative models and Bayesian inference.

Pranav Mamidanna

Max Günthner

Judith Schepers

Steffen Schneider

My goal is to build machine learning models capable of approaching the performance of biological brains in terms of data-efficiency and robustness to perturbations and changes in their environment. Drawing inspiration from adaptation behavior of biological systems, I study methods for domain adaptation, transfer learning and semi-supervised learning.